Advanced Analytics with PySpark Patterns for Learning from Data at Scale Using Python and Spark 1st Edition by Akash Tandon ,Sandy Ryza ,Uri Laserson ,Sean Owen ,Josh Wills ISBN 1098103653 978-1098103651

Advanced Analytics with PySpark Patterns for Learning from Data at Scale Using Python and Spark 1st Edition by Akash Tandon ,Sandy Ryza ,Uri Laserson ,Sean Owen ,Josh Wills – Ebook PDF Instant Download/Delivery:1098103653 ,978-1098103651
Full download Advanced Analytics with PySpark Patterns for Learning from Data at Scale Using Python and Spark 1st Edition after payment

Product details: 

ISBN 10:1098103653

ISBN 13:978-1098103651

Author:Akash Tandon ,Sandy Ryza ,Uri Laserson ,Sean Owen ,Josh Wills

The amount of data being generated today is staggering and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark’s Python API, and other best practices in Spark programming.

Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills offer an introduction to the Spark ecosystem, then dive into patterns that apply common techniques-including classification, clustering, collaborative filtering, and anomaly detection, to fields such as genomics, security, and finance. This updated edition also covers NLP and image processing.

If you have a basic understanding of machine learning and statistics and you program in Python, this book will get you started with large-scale data analysis.

• Familiarize yourself with Spark’s programming model and ecosystem
• Learn general approaches in data science
• Examine complete implementations that analyze large public datasets
• Discover which machine learning tools make sense for particular problems
• Explore code that can be adapted to many uses

Table of contents: 

1. Analyzing Big Data
   Working with Big Data
   Introducing Apache Spark and PySpark
   Components
   PySpark
   Ecosystem
   Spark 3.0
   PySpark Addresses Challenges of Data Science
   Where to Go from Here
2. Introduction to Data Analysis with PySpark
   Spark Architecture
   Installing PySpark
   Setting Up Our Data
   Analyzing Data with the DataFrame API
   Fast Summary Statistics for DataFrames
   Pivoting and Reshaping DataFrames
   Joining DataFrames and Selecting Features
   Scoring and Model Evaluation
   Where to Go from Here
3. Recommending Music and the Audioscrobbler Dataset
   Setting Up the Data
   Our Requirements for a Recommender System
   Alternating Least Squares Algorithm
   Preparing the Data
   Building a First Model
   Spot Checking Recommendations
   Evaluating Recommendation Quality
   Computing AUC
   Hyperparameter Selection
   Making Recommendations
   Where to Go from Here
4. Making Predictions with Decision Trees and Decision Forests
   Decision Trees and Forests
   Preparing the Data
   Our First Decision Tree
   Decision Tree Hyperparameters
   Tuning Decision Trees
   Categorical Features Revisited
   Random Forests
   Making Predictions
   Where to Go from Here
5. Anomaly Detection with K-means Clustering
   K-means Clustering
   Identifying Anomalous Network Traffic
   KDD Cup 1999 Dataset
   A First Take on Clustering
   Choosing k
   Visualization with SparkR
   Feature Normalization
   Categorical Variables
   Using Labels with Entropy
   Clustering in Action
   Where to Go from Here
6. Understanding Wikipedia with LDA and Spark NLP
   Latent Dirichlet Allocation
   LDA in PySpark
   Getting the Data
   Spark NLP
   Setting Up Your Environment
   Parsing the Data
   Preparing the Data Using Spark NLP
   TF-IDF
   Computing the TF-IDFs
   Creating Our LDA Model
   Where to Go from Here
7. Geospatial and Temporal Data Analysis on Taxi Trip Data
   Preparing the Data
   Converting Datetime Strings to Timestamps
   Handling Invalid Records
   Geospatial Analysis
   Intro to GeoJSON
   GeoPandas
   Sessionization in PySpark
   Building Sessions: Secondary Sorts in PySpark
   Where to Go from Here
8. Estimating Financial Risk
   Terminology
   Methods for Calculating VaR
   Variance-Covariance
   Historical Simulation
   Monte Carlo Simulation
   Our Model
   Getting the Data
   Preparing the Data
   Determining the Factor Weights
   Sampling
   The Multivariate Normal Distribution
   Running the Trials
   Visualizing the Distribution of Returns
   Where to Go from Here
9. Analyzing Genomics Data and the BDG Project
   Decoupling Storage from Modeling
   Setting Up ADAM
   Introduction to Working with Genomics Data Using ADAM
   File Format Conversion with the ADAM CLI
   Ingesting Genomics Data Using PySpark and ADAM
   Predicting Transcription Factor Binding Sites from ENCODE Data
   Where to Go from Here
10. Image Similarity Detection with Deep Learning and PySpark LSH
    PyTorch
    Installation
    Preparing the Data
    Resizing Images Using PyTorch
    Deep Learning Model for Vector Representation of Images
    Image Embeddings
    Import Image Embeddings into PySpark
    Image Similarity Search Using PySpark LSH
    Nearest Neighbor Search
    Where to Go from Here
11. Managing the Machine Learning Lifecycle with MLflow
    Machine Learning Lifecycle
    MLflow
    Experiment Tracking
    Managing and Serving ML Models
    Creating and Using MLflow Projects
    Where to Go from Here

People also search for:

big data analytics with pyspark

data analytics vs advanced analytics

is dynamodb good for analytics

advanced analytics using pyspark

spark and pyspark difference

Tags: Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills, Advanced, Analytics